Bolstering gun injury surveillance accuracy using capture–recapture methods

Lori Ann Post*, Zev Balsen, Richard Spano, Federico E. Vaca

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Using a single source of data, such as police records, or combining data from multiple sources results in an undercount of gun-related injuries. To improve gun-related injury surveillance accuracy by using capture–recapture methods, data were culled from law enforcement, emergency departments, emergency medical services, media, and medical examiner records. The data overlap was operationalized using capture–recapture to generate estimates of uncounted gun incidents. Dependencies between data sources were controlled using log-linear modeling for accurate estimates. New Haven, Connecticut. The study population included subjects injuried/killed from a gun projectile. Incidence was measured using capture–recapture. 49 gun injuries occurred within the defined geography. No single source recorded more than 43 gun-related injuries/deaths. Log-linear modeling estimated the actual number of injuries to be 49.1 (95% CI 49–49.9). Capture–recapture may be less useful in large metropolitan areas that cross state geographical boundaries because of how government agency data are aggregated within each state. No single data source achieves complete gun-related case ascertainment. Log-linear and capture–recapture methods significantly improve gun-related injury estimates.

Original languageEnglish (US)
Pages (from-to)674-680
Number of pages7
JournalJournal of Behavioral Medicine
Volume42
Issue number4
DOIs
StatePublished - Aug 15 2019

Keywords

  • Firearm violence
  • Gun case definition
  • Gun injury surveillance
  • Gun violence

ASJC Scopus subject areas

  • Psychology(all)
  • Psychiatry and Mental health

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